Co-induction of cyclooxyenase-2 and early growth response gene (Egr-1) in spinal cord in a clinical model of persistent inflammation and hyperalgesia

BackgroundThis study characterised the effects of persistent peripheral inflammation of the foot on pain and spinal cord expression of cyclooxygenase-1 and -2 (COX-1 and COX-2) and early growth response gene 1 (Egr-1), known markers of neuronal plasticity, in a clinical model of naturally-occurring inflammatory disease and hyperalgesia in sheep ('footrot'), before and after routine treatment (parenteral treatment with antibiotics and antiseptic footbathing). The temporal pattern of expression of COX-1, COX-2 and Egr-1 mRNA and protein were analysed using real-time PCR and Western blotting. ResultsAnimals affected with persistent peripheral inflammation displayed significant hyperalgesia and lameness (a proxy indicator of spontaneous pain) restricted to the inflamed limb. Hyperalgesia and lameness were significantly attenuated 1 day after treatment, and resolved further by day 7 and day 3, respectively. COX-2 but not COX-1, protein expression was up-regulated in spinal cord from lame animals on day 0, before treatment. Following treatment and attenuation of pain behaviours, levels of COX-2 returned to control levels. Significant induction of Egr-1 mRNA and protein were observed in spinal cord from lame animals. Three days after treatment, levels of Egr-1 mRNA returned to control levels, however, Egr-1 protein remained elevated. ConclusionElevated levels of spinal COX-2 and Egr-1 protein correlate with the presence of pain and hyperalgesia, and may underlie persistent pain, although a direct causal link has still to be established. Understanding the temporal pattern of expression of key mediators in clinical pain states may lead to better strategies to manage pai

Semi-supervised Learning for Photometric Supernova Classification

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72% (87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in Type Ia efficiency.Comment: 16 pages, 11 figures, accepted for publication in MNRA

Cognitive and neuroscientific perspectives of healthy ageing

With dementia incidence projected to escalate significantly within the next 25 years, the United Nations declared 2021–2030 the Decade of Healthy Ageing, emphasising cognition as a crucial element. As a leading discipline in cognition and ageing research, psychology is well-equipped to offer insights for translational research, clinical practice, and policy-making. In this comprehensive review, we discuss the current state of knowledge on age-related changes in cognition and psychological health. We discuss cognitive changes during ageing, including (a) heterogeneity in the rate, trajectory, and characteristics of decline experienced by older adults, (b) the role of cognitive reserve in age-related cognitive decline, and (c) the potential for cognitive training to slow this decline. We also examine ageing and cognition through multiple theoretical perspectives. We highlight critical unresolved issues, such as the disparate implications of subjective versus objective measures of cognitive decline and the insufficient evaluation of cognitive training programs. We suggest future research directions, and emphasise interdisciplinary collaboration to create a more comprehensive understanding of the factors that modulate cognitive ageing

The application of predictive modelling for determining bio-environmental factors affecting the distribution of blackflies (Diptera: Simuliidae) in the Gilgel Gibe watershed in Southwest Ethiopia

Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies

Hybrid GaN LED with capillary-bonded II–VI MQW color-converting membrane for visible light communications

The rapid emergence of gallium-nitride (GaN) light-emitting diodes (LEDs) for solid-state lighting has created a timely opportunity for optical communications using visible light. One important challenge to address this opportunity is to extend the wavelength coverage of GaN LEDs without compromising their modulation properties. Here, a hybrid source for emission at 540 nm consisting of a 450 nm GaN micro-sized LED (micro-LED) with a micron-thick ZnCdSe/ZnCdMgSe multi-quantum-well color-converting membrane is reported. The membrane is liquid-capillary-bonded directly onto the sapphire window of the micro-LED for full hybridization. At an injection current of 100 mA, the color-converted power was found to be 37 μW. At this same current, the −3 dB optical modulation bandwidth of the bare GaN and hybrid micro-LEDs were 79 and 51 MHz, respectively. The intrinsic bandwidth of the color-converting membrane was found to be power-density independent over the range of the micro-LED operation at 145 MHz, which corresponds to a mean carrier lifetime of 1.9 ns

Local exome sequences facilitate imputation of less common variants and increase power of genome wide association studies

The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%

Long‐term trends in the distribution, abundance and impact of native “injurious” weeds

Questions: How can we quantify changes in the distribution and abundance of injurious weed species (Senecio jacobaea, Cirsium vulgare, Cirsium arvense, Rumex obtusifolius, Rumex crispus and Urtica dioica), over long time periods at wide geographical scales? What impact do these species have on plant communities? To what extent are changes driven by anthropogenically induced drivers such as disturbance, eutrophication and management? Location: Great Britain. Methods: Data from national surveys were used to assess changes in the frequency and abundance of selected weed species between 1978 and 2007. This involved novel method development to create indices of change, and to relate changes in distribution and abundance of these species to plant community diversity and inferred changes in resource availability, disturbance and management. Results: Three of the six weed species became more widespread in GB over this period and all of them increased in abundance (in grasslands, arable habitats, roadsides and streamsides). Patterns were complex and varied by landscape context and habitat type. For most of the species, there were negative relationships between abundance, total plant species richness, grassland, wetland and woodland indicators. Each individual species responds to a different combination of anthropogenic drivers but disturbance, fertility and livestock management significantly influenced most species. Conclusions: The increase in frequency and abundance of weeds over decades has implications for landscape‐scale plant diversity, fodder yield and livestock health. This includes reductions in plant species richness, loss of valuable habitat specialists and homogenisation of vegetation communities. Increasing land‐use intensity, excessive nutrient input, overgrazing, sward damage, poaching and bare ground in fields and undermanagement or too frequent cutting on linear features may have led to increases in weeds. These weeds do have conservation value so we are not advocating eradication, rather co‐existence, without dominance. Land management policy needs to adapt to benefit biodiversity and agricultural productivity

Long Covid in adults discharged from UK hospitals after Covid-19 : a prospective, multicentre cohort study using the ISARIC WHO Clinical Characterisation Protocol

Funding: This work is supported by grants from: the National Institute for Health Research (NIHR) [award CO-CIN-01], the Medical Research Council [grant MC_PC_19059], the Imperial Biomedical Research Centre (NIHR Imperial BRC, grant P45058), the Health Protection Research Unit (HPRU) in Respiratory Infections at Imperial College London and NIHR HPRU in Emerging and Zoonotic Infections at University of Liverpool, both in partnership with Public Health England, [NIHR award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], and the Bill and Melinda Gates Foundation [OPP1209135], and Liverpool Experimental Cancer Medicine Centre (Grant Reference: C18616/A25153), NIHR Biomedical Research Centre at Imperial College London [IS-BRC-1215-20013], EU Platform for European Preparedness Against (Re-) emerging Epidemics 1 [FP7 project 602525] and NIHR Clinical Research Network for providing infrastructure support for this research. LT is a Wellcome Trust clinical career development fellow, supported by grant number 205228/Z/16/Z. This research was funded in part, by the Wellcome Trust. PJMO is supported by a NIHR Senior Investigator Award [award 201385].Background : This study sought to establish the long-term effects of Covid-19 following hospitalisation. Methods : 327 hospitalised participants, with SARS-CoV-2 infection were recruited into a prospective multicentre cohort study at least 3 months post-discharge. The primary outcome was self-reported recovery at least ninety days after initial Covid-19 symptom onset. Secondary outcomes included new symptoms, disability (Washington group short scale), breathlessness (MRC Dyspnoea scale) and quality of life (EQ5D-5L). Findings : 55% of participants reported not feeling fully recovered. 93% reported persistent symptoms, with fatigue the most common (83%), followed by breathlessness (54%). 47% reported an increase in MRC dyspnoea scale of at least one grade. New or worse disability was reported by 24% of participants. The EQ5D-5L summary index was significantly worse following acute illness (median difference 0.1 points on a scale of 0 to 1, IQR: -0.2 to 0.0). Females under the age of 50 years were five times less likely to report feeling recovered (adjusted OR 5.09, 95% CI 1.64 to 15.74), were more likely to have greater disability (adjusted OR 4.22, 95% CI 1.12 to 15.94), twice as likely to report worse fatigue (adjusted OR 2.06, 95% CI 0.81 to 3.31) and seven times more likely to become more breathless (adjusted OR 7.15, 95% CI 2.24 to 22.83) than men of the same age. Interpretation : Survivors of Covid-19 experienced long-term symptoms, new disability, increased breathlessness, and reduced quality of life. These findings were present in young, previously healthy working age adults, and were most common in younger females.Publisher PDFPeer reviewe

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases